Risk assessment due to electricity price forecast uncertainty in UK electricity market

G. Gao, K. Lo, Jianfeng Lu
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引用次数: 3

Abstract

This paper illustrates the risk assessment on electricity price forecast uncertainty. The high-risk periods under different time have been indicated. Autoregressive integrated moving average (ARIMA) models and artificial neural network (ANN) techniques are introduced to forecast electricity prices in UK electricity market. Also, this paper investigates the risk index of electricity prices due to forecast uncertainties in the competitive power market through two aspects — daily and seasonal. This risk index is calculated using the errors of short-term electricity price forecast. The input data of forecasting models is divided into weekday and weekend profiles and this is done to observe the different electricity price dynamic risks between weekdays and weekends.
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英国电力市场电价预测不确定性的风险评估
本文阐述了电价预测不确定性的风险评估。指出了不同时间下的高危期。引入自回归综合移动平均(ARIMA)模型和人工神经网络(ANN)技术对英国电力市场电价进行预测。此外,本文还从日、季节两个方面研究了竞争电力市场中由于预测不确定性导致的电价风险指数。该风险指数是利用短期电价预测误差计算得出的。将预测模型的输入数据分为工作日和周末两部分,观察工作日和周末电价动态风险的差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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